【摘要】 针对生化气体源参数测定问题,提出了一种基于传感网络的分布式贝叶斯迭代估计算法,该算法在给定气体物理分布扩散模型条件下,通过传感器节点获取气体浓度,并基于分布式扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)实现气体源的坐标定位和释放率估计.通过仿真实验对两种分布式算法进行性能分析,结果表明,UKF算法在参数估计成功率和参数估计误差两个方面均要好于EKF算法,分别可以提高约50%和70%,其收敛速度快,使用节点少,更有助于节省网络能量消耗,并延长其生存周期.更多还原

【Abstract】 A distributed iterative estimation method within Bayesian filtering framework was proposed for parameters determination of biochemical odor source using wireless sensor networks.Given the physical model of gas distribution,the distributed extended Kalman filter(EKF) and unscented Kalman filter(UKF) were used to localize the chemical source and determine its emission rate based on the concentration detected by wireless sensor nodes.Simulation results show that the success rate and parameters estimation accuracy of the UKF algorithm are 50% and 70% higher,respectively,than those of the EKF.The faster convergence and fewer node numbers of UKF resulted in less network energy consumption and more survival time.更多还原